Addressing High Latency in RAG Systems
High latency in time-to-first-token (TTFT) is a major issue for retrieval-augmented generation (RAG) systems. Traditional RAG systems process multiple document chunks to generate responses, which can be slow due to heavy computation. This is especially problematic for applications needing quick answers, like real-time question answering or content creation.
Introducing TurboRAG
Researchers from Moore Threads AI have developed TurboRAG, a new method that optimizes RAG systems by pre-computing and storing key-value (KV) caches offline. Instead of recalculating these caches during each request, TurboRAG uses pre-stored KV caches to speed up the process, reducing computational load and response times while maintaining accuracy.
How TurboRAG Works
TurboRAG operates in two phases:
- Offline Phase: KV caches for document chunks are computed and stored, minimizing online computation.
- Online Phase: When a query is received, TurboRAG retrieves the pre-computed KV caches and combines them with the user query to generate quick responses.
This system uses independent attention masks to avoid unnecessary cross-document attention and relative position embeddings to keep positional relationships intact, making it compatible with most large language models (LLMs) without needing major changes.
Benefits of TurboRAG
Experimental results show that TurboRAG can reduce TTFT by up to 9.4 times compared to traditional RAG systems, with an average speed increase of 8.6 times. It also cuts KV cache computation costs by over 98%, allowing for larger batch sizes and better throughput. Importantly, TurboRAG maintains similar accuracy to traditional methods even in challenging retrieval scenarios.
Conclusion: A Practical Solution for Fast Response Times
TurboRAG effectively resolves latency issues in RAG systems by separating the costly KV cache generation from the online inference process. By using pre-computed KV caches and optimizing attention mechanisms, TurboRAG enhances speed and efficiency while keeping accuracy intact. This makes TurboRAG an excellent choice for real-time and large-scale applications.
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